RAG systems need better document ingestion, not just better LLMs

This article argues that many failures in retrieval-augmented generation (RAG) systems stem from poor document ingestion rather than issues with the language models themselves. It proposes a "structural chunking" approach that prioritizes parsing documents for their inherent structure, such as sections, tables, and headings, before splitting text based on token limits. This method aims to create more coherent and contextually relevant chunks, improving retrieval quality, especially for visually complex documents where image-based retrieval might be more effective than text extraction. AI

IMPACT Improving document ingestion pipelines can significantly enhance RAG system performance and reduce reliance on more powerful LLMs.

RANK_REASON Article provides an opinion and technical analysis on improving RAG systems, rather than announcing a new product or research.

Read on dev.to — LLM tag →

• Acme Corporation
• ColPali
• LangChain
• PDF
• RecursiveCharacterTextSplitter
• retrieval-augmented generation

• infra
• product

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